filter_poi <- function(type, max_level) {
  return(poptimes %>% filter(poptimes[type] != 0 & poptimes[type] <= max_level))
}

normalize <- function(x) {
  return( (x - min(x) ) / ( max(x) - min(x) ) * 100)
}

summarize_hours <- function(input, func=mean) {
  df = input[,(ncol(input)-(168-1)):ncol(input)]
  df = as.data.frame(t(df %>% summarize_each(funs(func))))
  df$hour.total = 0:(nrow(df)-1)
  days = c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")
  df$day = days[floor((df$hour) / 24) + 1]
  df$hour.day = df$hour %% 24
  colnames(df)[1] = "popularity"
  df$popularity = normalize(df$popularity)
  df = df[, c(3, 2, 1, 4)]
  return(df)
}
poptimes <- read_csv("tidy-places.csv")
poi <- c("supermarket", "church", "gym", "shopping_mall", "store", "department_store")

filtered <- lapply(poi, filter_poi, 2)

bar <- filter_poi("bar", 1)
restaurant <- filter_poi("restaurant", 1)
supermarket <- as.data.frame(filtered[match("supermarket", poi)])
gym <- as.data.frame(filtered[match("gym", poi)])
shopping_mall <- as.data.frame(filtered[match("shopping_mall", poi)])
department_store <- as.data.frame(filtered[match("department_store", poi)])
store <- union(shopping_mall, department_store)
plot_hours <- function(input, plot_title=NULL, day_filter=NULL) {
  x_breaks = seq(0, 24, 6)
  y_breaks = seq(0, 100, 10)
  if (!is.null(day_filter)) {
    input = input %>% filter(day == day_filter)
    x_breaks = seq(0, 23, 1)
  }
  plot = ggplot(data = input, mapping = aes(x=hour.day, y=popularity)) +
    geom_bar(stat="identity", fill="dodgerblue4", color="white", width=1) + 
    facet_wrap(~factor(day,levels=c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")), nrow=1) +
    labs(title=plot_title, x="Hour of Day", y="Popularity") +
    scale_x_continuous(breaks=x_breaks, limits=c(0, 24), na.value=0) +
    scale_y_continuous(breaks=y_breaks, limits=c(0, 100)) +
    theme_minimal() +
    theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank())
  return(plot)
}

plot_hours_daily <- function(input, plot_title=NULL) {
  days = c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")
  for(day in days) {
    print(plot_hours(input, plot_title, day))
  }
}

plot_hours_individual <- function(input) {
  for (x in 1:nrow(input)) {
    name = input[x,"name"]
    addr = input[x,"address"]
    print(plot_hours(summarize_hours(input[x,], mean), paste(name, addr, sep=" // ")))
  }
}

avg_time_spent <- function(input, type) {
  data = input %>% filter(!is.na(time_spent_1) & !is.na(time_spent_2))
  t1 = mean(data$time_spent_1)
  t2 = mean(data$time_spent_2)
  df = data.frame("type"=type, "time_spent_1"=t1, "time_spent_2"=t2)
  return(df)
}

Average Time Spent per Location Type

loc <- list(bar, restaurant, supermarket, gym, shopping_mall, department_store)
loc_name <- c("bar", "restaurant", "supermarket", "gym", "shopping mall", "department store")

avg_time <- data.frame("type"=".", "time_spent_1"=0.1, "time_spent_2"=0.1)
for (i in 1:length(loc)) {
  avg_time <- union(avg_time, as.data.frame(avg_time_spent(as.data.frame(loc[i]), loc_name[i])))
}
avg_time <- avg_time[2:nrow(avg_time),]
avg_time <- avg_time %>% mutate(time_spent_diff = time_spent_2-time_spent_1)

kable(avg_time %>% arrange(time_spent_1), caption = "Sort by time_spent_1")
Sort by time_spent_1
type time_spent_1 time_spent_2 time_spent_diff
supermarket 15.00000 15.00000 0.00000
gym 15.00000 58.00000 43.00000
shopping mall 15.83333 28.33333 12.50000
department store 18.50000 24.50000 6.00000
restaurant 18.76712 41.36986 22.60274
bar 42.75000 83.00000 40.25000 
kable(avg_time %>% arrange(time_spent_2), caption = "Sort by time_spent_2")
Sort by time_spent_2
type time_spent_1 time_spent_2 time_spent_diff
supermarket 15.00000 15.00000 0.00000
department store 18.50000 24.50000 6.00000
shopping mall 15.83333 28.33333 12.50000
restaurant 18.76712 41.36986 22.60274
gym 15.00000 58.00000 43.00000
bar 42.75000 83.00000 40.25000 
kable(avg_time %>% arrange(time_spent_diff), caption = "Sort by time_spent_diff")
Sort by time_spent_diff
type time_spent_1 time_spent_2 time_spent_diff
supermarket 15.00000 15.00000 0.00000
department store 18.50000 24.50000 6.00000
shopping mall 15.83333 28.33333 12.50000
restaurant 18.76712 41.36986 22.60274
bar 42.75000 83.00000 40.25000
gym 15.00000 58.00000 43.00000